The global computerized maintenance management system (CMMS) market is anticipated to showcase promising growth over the assessment period by registering a CAGR of 9.1% from 2023 to 2033. The global market revenue totaled US$ 1,

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Datamyne

datamyne

On-chain analysis is a process where someone (i.e., a crypto trader) looks at the on-chain data to help them make decisions. On-chain data often comprises information on every transaction on a particular public blockchain network. Information, including transactions, wallet addresses, transfer amounts, and more, are stored on the public ledger. So anyone can use a block explorer to find this information. Users can also use block explorers to review miner fees, smart contract code, and more. The information obtained from an on-chain study enables anyone to determine who owns what and their transaction activity. For example, users can follow multiple transactions to trace their movements and determine where the payment originally came from. All of this information is recorded on the public ledger for anyone to see. When it comes to trading, on-chain analysis is a method of financial research driven by fundamentals rather than technical indicators. Instead of looking for patterns on a chart, traders look at the recorded activity on the public ledger. On-chain analysis focuses only on a particular cryptocurrency asset by investigating its transaction activity and historical patterns, all of which may be used to anticipate the direction the cryptocurrency will go. Trade data and investment activity may be retrieved from the blockchain network and evaluated to help investors create a viable trading strategy.  For example, users can create models to help them make trading choices based on historical data. While history does not always repeat itself, it does rhyme, so being able to see how assets have behaved in the past is a good indicator for future strategies. Additionally, people may use the gathered financial data to create prediction models that can effectively forecast future price movements. In addition to traditional research methods, including technical analysis (TA), on-chain analysis is progressively becoming more important for any investor serious about trading cryptocurrencies. Therefore, it is beneficial to approach the analysis of crypto assets from multiple angles instead of focusing on one approach. A wide variety of online platforms and tools enable users to gather and analyze on-chain data. For example, block explorers like EtherScan (for Ethereum) and BscScan (for Binance Chain) provide basic amounts of information regarding transactions on those networks. However, for deeper information gathering, more sophisticated tools are needed. , a leading on-chain data platform, has become the official provider of on-chain data for CME Group’s Datamine. This on-demand platform allows traditional financial institutions to access detailed historical data. CryptoQuant Being the first and only on-chain data provider of CME Datamine in July 2022, CryptoQuant offers its data via CME Datamine, which enables traditional financial institutions and hedge funds to purchase the data from the CME Group platform. They can now get digital asset data from CryptoQuant via the service. CME Group Datamine CryptoQuant data is recognized globally as the most reliable source of complex on-chain data for Bitcoin, such as exchange flows, miner flows, long-term holders, and many others. Used by many trusted media such as Forbes, Bloomberg, and CNBC, CryptoQuant has also worked with Valkyrie Fund to develop an overview of the Bitcoin market, contributing to Valkyrie’s Bitcoin Futures ETF approval in the US. Previously, CryptoQuant developed a patented wallet verification technology that served anti-money-laundering services in 2019. CryptoQuant collaborated with the Korean National Police Agency in tracking and arresting the infamous criminals behind the Nth Room . CryptoQuant has since grown exponentially, used by retail traders in over 200 countries and over 200 institutional clients. scandal “Digital asset investment was considered dangerous speculation compared to other assets such as stocks, gold, or properties. This bias has formed as there was no reliable information in this industry, and investors were purchasing digital assets without a data-based analysis,” said CryptoQuant CEO Ki Young Ju. “CryptoQuant’s main goal is to enable investors to redefine the way of digital asset investment, and to provide fundamental for digital asset valuation based on the data.” As more traditional financial institutions expand to digital assets, the importance of reliable on-chain data, delivered through already familiar tools, will increase ever more. With the partnership with CME Group, CryptoQuant is expected to become the staple of the traditional financial industry and is actively recruiting new hires to meet the institutions’ demands. CryptoQuant is backed by Hashed, Galaxy Interactive and other major ecosystem investors. Conclusion If you decide to use blockchain-based assets like cryptocurrency for your financial dealings, that data will be stored permanently and cannot be altered. Everyone has access to some of the data stored in a blockchain, including the results of any on-chain analysis conducted on that data. However, it is worth noting that on-chain data can be useful for formulating future trading strategies. Only the savviest traders and investors recognize the significance of the data recorded on the blockchain when determining how to best approach cryptocurrency transactions.

The Importance Of On-chain Analysis

A practical use case of Apache Kafka Data come in different shapes and forms. In the real world, a lot of data are generated in streams. Streaming data is a continuous flow of events generated by various sources. With the ubiquitousness of mobile devices, social media, IoT and big data platforms, more and more data sources offer streaming data for analytics consumption. Real time analytics of streaming data differs from batch data in that analytics are updated based on a few new data events, instead of the entire dataset. Some examples of streaming data are Facebook/Instagram/Twitter posts Sensor data sent from airplane engines Time series data from financial stocks trading on exchanges In recent years, has become the technology of choice when it comes to working with streaming data. It offers persistent storage that guarantees message ordering, and a pub-sub semantics that are easy to reason with. It can handle terabyte throughput easily and is a popular choice for any company that works with massive amount of event-driven data. Apache Kafka MTA, the agency operating NYC subways, publishes real time subway trip updates via a that are available to the public. set of RESTful APIs In this post we will use Apache Kafka to build a real time NYC subway tracker that shows you when the next train will arrive in the station. Here at Cloudbox Labs, building cloud native applications is our business. In that spirit, we will host our Kafka cluster on Amazon Web Services (AWS) and our application on EC2. The overall architecture of our system looks like this In order to interact with Kafka pub-sub model, we will write a message producer that generates message streams and publish them onto Kafka. MTA’s data stream refreshes every 30 seconds so our producer will query the API for the latest subway trip updates and publish these updates to a Kafka topic. On the other end we will spin up a message consumer that listens on a particular topic and process the update messages as they come through. The consumer keeps an in-memory data structure that stores timing of the next arrival train for each station by subway line. For demo purpose our Kafka cluster setup is fairly simple. We set up a two node Kafka cluster so we can take advantage of its distributed and replicated storage capability. We created one topic that will store our message queue. With that we are ready to interact with the Kafka cluster. MTA’s trip update API uses in specification so the data has a standard format. In order to parse the response from the API we need to first auto-generate the python protobuf classes based on protobuf gtfs-realtime feed gtfs-realtime.proto python -m grpc_tools.protoc -Iprotos/ --python_out=. --grpc_python_out=. protos/gtfs-realtime.proto Using the Confluent Kafka python client, writing Kafka producer and consumer are fairly easy. Here is the code snippet of our trip update producer import time import requestsfrom google.protobuf.json_format import MessageToJsonfrom confluent_kafka import Producer import gtfs_realtime_pb2 class MTARealTime(object): def \_\_init\_\_(self):  
    with open('.mta\_api\_key', 'r') as key\_in:  
        self.api\_key = key\_in.read().strip()  

    self.mta\_api\_url = 'http://datamine.mta.info/mta\_esi.php?key={}&feed\_id=1'.format(  
        self.api\_key)  
    self.kafka\_topic = 'test'  
    self.kafka\_producer = Producer({'bootstrap.servers': 'localhost:9092'})  

def produce\_trip\_updates(self):  
    feed = gtfs\_realtime\_pb2.FeedMessage()  
    response = requests.get(self.mta\_api\_url)  
    feed.ParseFromString(response.content)  

    for entity in feed.entity:  
        if entity.HasField('trip\_update'):  
            update\_json = MessageToJson(entity.trip\_update)  
            self.kafka\_producer.produce(  
                self.kafka\_topic, update\_json.encode('utf-8'))  

    self.kafka\_producer.flush()  

def run(self):  
    while True:  
        self.produce\_trip\_updates()  
        time.sleep(30) And here is our trip update consumer import csvfrom collections import defaultdictimport json import arrowfrom confluent_kafka import Consumer, KafkaError class MTATrainTracker(object): def \_\_init\_\_(self):  
    self.kafka\_consumer = Consumer({  
        'bootstrap.servers': 'localhost:9092',  
        'group.id': 'test\_consumer\_group',  
        'default.topic.config': {  
            'auto.offset.reset': 'smallest'  
        }  
    })  
    self.kafka\_topic = 'test'  

    # subway line number -> (stop\_id, direction) -> next arrival time  
    self.arrival\_times = defaultdict(lambda: defaultdict(lambda: -1))  

    self.stations = {}  
    with open('static/mta\_stations.csv') as csvf:  
        reader = csv.DictReader(csvf)  
        for row in reader:  
            self.stations\[row\['GTFS Stop ID'\]\] = row\['Stop Name'\]  

def process\_message(self, message):  
    trip\_update = json.loads(message)  

    trip\_header = trip\_update.get('trip')  
    if not trip\_header:  
        return  

    route\_id = trip\_header\['routeId'\]  
    stop\_time\_updates = trip\_update.get('stopTimeUpdate')  
    if not stop\_time\_updates:  
        return  

    for update in stop\_time\_updates:  
        if 'arrival' not in update or 'stopId' not in update:  
            continue  

        stop\_id, direction = update\['stopId'\]\[0:3\], update\['stopId'\]\[3:\]  
        new\_arrival\_ts = int(update\['arrival'\]\['time'\])  

        next\_arrival\_ts = self.arrival\_times\[route\_id\]\[(stop\_id, direction)\]  
        now = arrow.now(tz='US/Eastern')  

        if new\_arrival\_ts >= now.timestamp and \\  
                (next\_arrival\_ts == -1 or new\_arrival\_ts < next\_arrival\_ts):  
            self.arrival\_times\[route\_id\]\[(stop\_id, direction)\] = new\_arrival\_ts  

            # convert time delta to minutes  
            time\_delta = arrow.get(new\_arrival\_ts) - now  
            minutes = divmod(divmod(time\_delta.seconds, 3600)\[1\], 60)\[0\]  
            print('Next {} bound {} train will arrive at station {} in {} minutes'.format(  
                direction, route\_id, self.stations\[stop\_id\], minutes))  

def run(self):  
    self.kafka\_consumer.subscribe(\[self.kafka\_topic\])  

    while True:  
        msg = self.kafka\_consumer.poll(1.0)  

        if msg is None or not msg.value():  
            continue  
        if msg.error() and msg.error().code() != KafkaError.\_PARTITION\_EOF:  
            raise ValueError('Kafka consumer exception: {}'.format(msg.error()))  

        msg = msg.value()  
        self.process\_message(msg.decode('utf-8')) Both the Kafka cluster and the application components run on separate EC2 on AWS. Once we spin up all the pieces, we can see that the consumer is listing the next train arrival time for each station Next S bound 1 train will arrive at station Rector St in 0 minutes Next S bound 1 train will arrive at station South Ferry in 2 minutes Next S bound 1 train will arrive at station Cortlandt St in 0 minutes Next N bound 6 train will arrive at station Spring St in 4 minutes Next N bound 6 train will arrive at station Bleecker St in 5 minutes Next N bound 6 train will arrive at station Astor Pl in 6 minutes Streaming data has major applications in modern data analytics. At Cloudbox Labs, we love building stream processing platforms so our clients can analyze data faster and at scale. As always you can find the full code discussed in this post on . Cloudbox Labs github Tags: , , , , , , Amazon Web Services Apache Kafka gtfs-realtime NYC subway tracker protocol buffer rest API streaming data

Building a real time NYC subway tracker with Apache Kafka

Charlemagne crowned Holy Roman Emperor, proclaims GDPR Admittedly we’ve been largely silent on the topic of the new European General Data Protection Rules (GDPR) because frankly, we don’t really know how to deal with it. The reality is, really knows how to deal with it. The Registrar Stakeholders Group (RRSG) has been very frustrated, and ICANN, never missing an opportunity to avoid an issue, has been little help (they’ve issued a temporary specification allowing registrars to not display whois details, but they did not grant a requested moratorium on compliance. The obligation to collect said data still applies. At least one registrar, EPAG, based in Germany, has ceased collecting that data because they believe doing so violates GDPR, and ICANN has duly against them.) nobody filed an injunction We consulted with our external counsel who handled our positioning for the Canadian Anti Spam Legislation (CASL) who told us, in effect, “we don’t know what you should do either, just do what Tucows does”. In practice, because we use the OpenHRS/Tucows registry stack, we’re largely constrained by whatever they do, and for now, they’re redacting whois across the board, even for non-European registrants. Some registries are also following suit. (Tucows also happens to be the parent company of EPAG, mentioned above). The whois database is the big thing across the entire domain business. All registrars are contractually obligated under the terms of their accreditation to publish whois records containing contact info for domain registrants, but doing so , seemingly runs afoul of GDPR. for European registrants But it suddenly hit me as I thought about this more it reminded me of 2016, when changes in the European VAT shifted from those taxes being applied to the location of the vendor to being applied at the location of the consumer. That supposedly meant that on their European clients and remit it back to Europe quarterly. all businesses around the world would have to collect VAT Uh yeah, we’re not going to be doing that. So following on that theme… Why should any business outside of the EU care what the GDPR contains? How is it different from when some foreign court hands down a court order pertaining to one of our customers which we will not take action on until they obtain proper letters rogatory to have it enforced in our jurisdiction? When the FDA sent us a list of domains belonging to Canadian companies that were fully compliant with Health Canada demanding we take them down, did we do it? If we suddenly receive a court order from afar in a language we can’t even read are we going to hire a translator and then do whatever it tells us to do? (Turns out it was Polish and we had somebody here who could read it, but we still didn’t do what it was ordering us to do). When China wants us to prevent any citizen from using our dynamic DNS service which would enable them to circumvent their Great Firewall of China are we going to hire a team of developers to code it into our system? (We were once offered exactly that deal by a large router company wanting to setup a dyndns business there and we turned it down). No, no and no. So why are non-European companies frantically running around, updating their privacy policies, trying to re-permission all their mailing lists, , trying to over-comply with something that at most only affects non-business customers in EU member states? even to non-European users All prior history of internet governance would have us taking the position that being a Canadian company we are going to operate by Canadian law, and not by European law. Here we have PIPEDA which covers data privacy and more recently the CASL, anti-spam legislation. Further, the way CIRA operates the whois system for .CA — where individual registrants have their data redacted by default and companies have it displayed; seems to have hit a happy medium which has more or less worked fine for nearly 20 years. But until such time as the EU enters into an international treaty with Canada to have us enforce the GDPR and collect VAT (oh yeah, I’m gonna vote for the hack who campaigns on that platform), why do we actually care what the GDPR says? totally By even pretending to acknowledge the GDPR’s applicability to non-Euro businesses, we open ourselves up to having to comply with every other political jurisdiction in the world, now and in the future, such as , which really a bag of crap that contains provisions for things like mandatory ISP filtering on user uploaded content and “link taxes”. Europe’s forthcoming Copyright Reform is Sure, Google and Facebook and Apple have to worry about this, because they’ve domiciled their foreign HQ’s in Ireland so that they can shelter all that foreign revenue from US taxation. Karma’s a bitch. But for the rest of us small fries, who pay taxes on our revenues here in our home countries, and who are sheltering billions of dollars in offshore tax havens that happen to be under EU rules, can somebody explain to me how this is in any way our problem? do all not But GDPR gives us the opportunity to be better companies So says , and I totally respect his ethos and his aspirations to be a better company. But here’s the thing: Everybody had the opportunity to be better companies all along. Some of us already took that opportunity: easyDNS doesn’t sell our customer data, we don’t datamine it, we don’t track them across the Internet, we’re generally known for our transparency and we don’t take liberties with our clients’ confidence . this post by Hansel Dunlop over on Medium Dunlop warns us: I would be very wary of a company who claims this legislation is onerous. It is potentially life threatening to companies who do very shady things without your consent. That much is true. That is the entire point. I have two responses to this. First, increasing regulatory and compliance burdens are very much asymmetrically adverse to small, medium and independent businesses. Larger companies have greater ability to absorb the additional compliance hurdles, have the market share to pass on any of their additional compliance costs to their customers frequently use their position and influence to lobby and shape legislation so that it often skews in their favour. and Incrementally raising compliance burdens is an actual business tactic, as related to me by somebody I’d call “a crony capitalist insider” who once organized a meeting for me to talk to some folks from one of the big four Canadian banks about Bitcoin. What he told me, point blank was that the way business works was to work policy makers to keep introducing new rules and regulations so that over time, the smaller and weaker competitors would be squeezed out and the larger incumbents could absorb their customers. with They way he talked about the prospect of being first mover in the Bitcoin space was very telling: “Here’s what you need to do if you want to make a go of this. You get big institutional money behind you. You approach the regulators, and you start working with those regulators to shape the actual rules that are going to come out. When the regulations finally come out, you get to be on the right side of them; and then you turn around and you pull the ladder up behind you and bingo. You’re a monopoly.” I always felt that meeting was very educational but I had to take a shower the moment I got home. It was more evidence to what I already suspected, increasing regulatory burdens are usually more about consolidating markets than protecting consumers. I’m not saying that’s the case with GDPR, but I am saying that the additional burden of compliance always does hit the smaller companies harder than the 800lb gorillas. My second counter to Dunlop, (but not aiming at his company), is that I would be skeptical of companies who are suddenly getting religion around their clients’ privacy and personal data sovereignty solely because they think they are to do so. That’s not reassuring. You know those companies will do the bare minimum, you know they’ll be tempted to cut corners and you know they’ll game it. And if they follow the same line of reasoning we are here and surmise that GDPR isn’t applicable to them they’re going to abrogate it anyway. legally compelled (All those re-permissioning email requests your getting? My prediction is first thing that happens is everybody craps themselves because they’ll be lucky to pull a 20% confirmation rate and they realize that if they go through with this then they just killed their lists. Then they’ll pretend it never happened and keep emailing their entire list anyway. If you’ve used proper list building techniques from the outset, you don’t need to re-permission even if you’re within the EU, because you have opt-in and confirmation data for all your subscribers or else you have an active business relationship with them like CASL here in Canada, right?) At the end of the day, a culture of customer respect is either baked-in to the company ethos or it’s conspicuous in its absence. A new set of regulations passed by faceless, unelected bureaucrats in another jurisdiction isn’t going to magically put it there. Like ICANN’s Whois Accuracy Program (WAP), and some complaints I have about CASL, all most new regulations typically do is make life harder for rule followers while not really doing anything about the rule breakers (except maybe to turn what were formerly rule followers rule breakers by hiking the compliance burden over their heads). into For the most part, easyDNS already complies with most of the GDPR that I’m familiar with anyway. In general we deal primarily with data that our clients the entire world to see, and we don’t store any of your billing details. As per the lawyers’ advice we’ll be adjusting our data retention policy which will more clearly specify when we delete your data after you cease being a client, but we’re doing so to make sure we’re onside with PIPEDA, which is Canada’s data privacy laws, not to be onside with Europe’s. want But as long as we keep doing of doing right , hopefully that day will never come. our job by you Originally published at easydns.com on May 28, 2018.

Why should any non-Euro companies care about the GDPR?

The components of the data-driven culture Modern companies are typically overloaded with large volumes of heterogeneous data, from multiple sources. They make significant investments on technologies and architectures such as data marts, data warehouses, data lakes and operational data stores with sophisticated on top. Business Intelligence, Analytics and Predictive systems Nevertheless, data & technology does not ensure better decisions unless an additional component is added to the mix: . the data-driven culture The “Data-driven” here implies much more than just technology and data. It means you need a special mentality. A leadership attitude. This makes it possible for your company to make informed decisions by using data systematically to improve business performance. The data-driven culture As the data engineering lead at , I’ve seen several companies struggle to establish this data-driven culture. datamine decision support systems Check also: how to establish an innovation culture These are companies with sophisticated data-warehouses, who are failing to add business professionals in the data analysis loop. In some cases they’re abandoning advanced analytical systems due to a lack of readiness by the company and lack of adoption by specific leaders or teams. To drive the , corporations need to first establish a stream of clean, accurate, reliable and active data feeds reflecting corporation’s business activities. All these qualities are critical for employees to - any limitation can prove to be the single point of failure in making the data-driven vision happen. data-driven culture trust the data, the technology and the tools Assuming the basics are there, employees need to start experiencing the benefits of being data driven. It’s important for them to understand that, by using data, they can improve their decisions**.** Their jobs can be more efficient with a . significant and measurable impact on the overall business Here are several ways to push employees in this direction: Share information & knowledge on data models, tools, reports and insights via quick-start sessions Systematically with possible follow ups in order to ask for interpretation and ideas/proposals on how to make them share insights and interesting data findings actionable on how the use of data allowed with real financial gains for the company Share real success stories smart decisions and data strategy of the company. Ask for ideas, proposals, requests Share the vision Capture needs and requests for new reports, additional data points, or new data analysis capabilities, data models, visualizations Ask for feedback on the overall data-driven experience. with known, hidden patterns in your data (artificial or real ones) and asks teams to discover them Run mini-hackathons Establish on data analysis and reporting tools formal training sessions Make data analysis a must for every business proposal, idea, business case The self-service insights store Every employee should be able to easily understand the performance of the company, the competitors, the corresponding local and global economic environment and the market dynamics. Employees need to build a habit of consuming well-defined (and designed) dashboards presenting and other insights, along with the outcomes of particular business decisions. business performance, trends, social signals As employees become data-aware and the complexity of data increases, a new class of systems is needed: a to empower business users perform their own information exploitation, analysis and visualization; to submit questions, consume dashboards, predefined reports; to dynamically generate reports via simple user interfaces; to easily define their what-if scenarios and get instant answers. self-service insights platform While the typical data warehouses and data lakes target particular classes of users (like data analysts and data scientists) the is open to any authorized employee - no special skills required. self-service insights store It’s an easy-to-use data analysis surface (powered by a range of data technologies, analytical and reporting engines) where and related reporting elements. insights and analytics are exposed in a modular form as active, reusable widgets This could take the form of a configurable and adaptive dashboard system which is based on an expandable range or predefined, active insight widgets. This could eventually become a repository of insights where new widgets get deployed and become usable by employees through custom dashboards and integration scenarios: Employees can easily reuse these insight widgets and combine them in customized, named dashboards which better serve their business needs. self-service insights store Personalized Insight experiences Interactive data insights stories provide a modern way to package and communicate data findings and interesting patterns, within the corporation. Either manually or automatically generated by AI, data stories can present a particular aspect of the business, the impact of certain decisions, and the results of large-scale online experiments. The focus is on the user experience so that . the story is delivered in a smooth, interactive way with multiple dimensions and entry points, taking into consideration the perspective of particular users according to their role, seniority and department within the organization Insights from smart buildings Smart buildings could be directly connected to the self-service insights store in order to access white-listed widgets (suitable for exposure within the building) and visualize business insights through the network of public screens within the corporate buildings. This experience could be (to the space, seasonality, team) delivering relevant insights to the right users at the right time. For instance public screens in the sales team area present sales performance metrics while in IT they experience network utilization KPIs. contextual Thanks for reading. Here are some other articles I’ve written that you might enjoy. How to run a successful corporate hackathon How to establish an ‘Innovation culture’ A business ecosystem driven by unemployed people

The data-driven corporation

This paper is available on arxiv under CC 4.0 license. Authors: (1) Vogt, Lars, TIB Leibniz Information Centre for Science and Technology; (2) Konrad, Marcel, TIB Leibniz Information Centre for Science and Technology; (3) Prinz, Manuel, TIB Leibniz Information Centre for Science and Technology. Table of Links Abstract & Introduction Interoperability Semantic interoperability and what natural languages like English can teach us Requirements for successfully communicating terms and statements Parallels between the structure of natural language statements and data schemata with implications for semantic interoperability What makes a term a good term and a schema a good schema? The need for a machine-actionable Rosetta Stone for (meta)data that acts as an interlingua for specifying reference terms and reference schemata to support cognitive and semantic interoperability Rosetta Stone and machine-readability: UPRIs, XML Schema datatypes, and RDF for communicating terms, datatypes, and statements Rosetta Stone and machine-interpretability: Wikidata and a modeling paradigm for (meta)data statements based on English Rosetta Stone and semantic interoperability: Specifying term mappings and schema crosswalks Rosetta Stone and cognitive interoperability: Specifying display templates and using a query builder Discussion Related work Conclusion, Acknowledgements, & References Rosetta Stone and machine-readability: UPRIs, XML Schema datatypes, and RDF for communicating terms, datatypes, and statements We have discussed above that a dataset documented in an ASCII file already qualifies as machine-readable. This readability includes all the terms and statements contained in the file. However, the ASCII file alone does not enable the machine to identify where exactly a term, a value, and a statement begin and end in a dataset. We would need to specify additional information in an application to enable a machine to make such distinctions. Because we want to communicate (meta)data efficiently and reliably across machines and between machines and humans, and because we want to disambiguate terms, values, and statements, we suggest using to communicate terms, to communicate datatypes, and to communicate statements. We use UPRIs for: UPRIs XML Schema datatypes RDF : instance resource, including named individuals, that use proper names as
their labels, refer to individual entities, and instantiate classes; instance-terms class resources that use general/kind terms as their labels and refer to the
extension of their class, i.e. the set of instance resources that instantiate the class.
Class-terms can also refer to types of statements and their defining verbs or predicates. class-terms: property resources that use verbs or predicates as their labels and refer to a
specific type of action or attribute. property-terms: Machines are able to recognize unambiguously where a UPRI or a value (i.e., a literal) associated with an XML Schema datatype begins and where it ends. Both can be used in RDF to make statements using the RDF triple syntax of , and a machine has no problem to recognize where a triple starts and where it ends. Subject-Predicate-Object Unfortunately, if you look at the predicate-argument-structure and compare the structure of triples with that of syntax trees, you will see that they are quite different and therefore do not properly align: the Predicate of a triple is always and necessarily , i.e., triples always have exactly one subject and one object argument. The predicates of natural language statements, however, are not necessarily always binary, as in the statement , but , as in ; “ travels by from to on the ”. Therefore, ontology properties (i.e., the resources used in triples) do not map in a one-to-one relation to natural language predicates, and we often need to model natural language statements using multiple triples (cf. Fig. 1 and Fig. 2E). This is possible in principle: If a triple uses a UPRI in its position that is used by another triple in its position, these two triples combine to form a semantic graph, so we can use multiple triples to model an n-ary natural language statement. However, semantic modeling of n-ary statements in RDF is often challenging, significantly increasing the semantification burden and usually resulting in graphs that are overly complex for humans to comprehend. binary “This tree has part an apple” n-ary “This apple has a weight of 212.45 grams” Anna train Berlin Paris 21st of April 2023 Predicate Subject Object In the following, we will stay with the RDF framework, as we believe it is the most flexible, allowing the specification of both ontologies and (meta)data schemata, as well as term mappings across ontologies and schema crosswalks across schemata. However, we will introduce a modeling paradigm that reflects the structure of English and provides a generic pattern for modeling n-ary statements in RDF. In all of this, we are trying to take a pragmatic approach that may not satisfy all the requirements for knowledge management that one would wish for in an ideal world, but from which we hope to achieve practical improvements.

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